Association of people thru their faces across cameras and time is a demanding need for wide area surveillance. Given that faces are detected, they need to be associated to form a track, and also they need to be associated across cameras or time in order to build a better understanding where the person was within the surveyed site or other sites at different times.
Eigenfaces are a set of eigenvectors used in the computer vision problem of human face recognition. The approach of using eigenfaces for recognition was developed by Matthew Turk and Alex Pentland beginning in 1987, and is considered the first facial recognition technology that worked. These eigenvectors are derived from a covariance matrix of the probability distribution of the high dimensional vector space of possible faces of human beings.
An eigenvector of a matrix is a vector such that, if multiplied with the matrix, the result is always an integer multiple of that vector. This integer value is the corresponding eigenvalue of the eigenvector. This relationship can be described by the equation M×u=λ×u, where u is an eigenvector of the matrix M and λ is the corresponding eigenvalue.